Presenter:

Authors:

Panagiota Galanakou(Physics, Florida Atlantic Univ)

Theodora Leventouri(Physics, Florida Atlantic Univ)

Georgios Kalantzis(Physics, Florida Atlantic Univ)

The purpose of this study is to elucidate the performance improvement of the simulating annealing algorithm (SAA) by parallelizing it on graphics processing unit (GPU) in highly dimensional optimization tasks, such as the Intensity Modulated Radiation Therapy (IMRT) in prostate and lung cancer cases. A MATLAB based implementation of treatment planning for radiation therapy was accomplished by using the computational environment for radiotherapy research (CERR). The planning target volume (PTV) was defined as a quadratic error function, while dose-volume constraints (DVCs) were applied for the dose that the Organs at risk (OARs) would receive separately. The SAA was implemented to determine the optimal intensities that deliver the prescribed dose in the PTV, while satisfying the dose-volume constraints for the OARs. For the parallelization of SAA on the GPU, the Parallel Computing Toolbox in MATLAB version 2016a was employed and the code was launched on four different GPUs. The performance comparison between the different GPUs was established on the speedup factors between the serial and parallelized SAA for different beamlets sizes. In prostate and lung cancer cases, a maximum speedup factor of ~33 for 0.2x0.2 cm2 beamlet size was achieved when the K40m card was utilized.

To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.MAR.V46.13